The receptor‐receptor interaction between mGluR1 receptor and NMDA receptor: a potential therapeutic target for protection against ischemic stroke
Bibliographic record
Abstract
Ischemic stroke is one of the leading causes of long‐term disability worldwide. It arises when the blood flow to the brain is severely impaired, causing brain infarction. The current therapies for ischemic stroke are tissue plasminogen activator and mechanical thrombectomy, which re‐establishes blood circulation to the brain but offers no neuroprotective effects. Excitotoxicity, particularly through the N‐methyl‐D‐aspartate receptor (NMDAR), has been heavily implicated in the pathophysiology of brain infarction resulting from ischemic stroke. Here we investigated the interaction between NMDAR and metabotropic glutamate receptor 1 (mGluR1) as a novel target to develop potential neuroprotective agents for ischemic stroke. Through coimmunoprecipitation and affinity binding assay, we revealed that the interaction is mediated through 2 distinct sites on the mGluR1 C terminus. We then found that the disruption of mGluR1‐GluN2A subunit of NMDAR (GluN2A) protected the primary mouse hippocampal neurons against NMDAR‐mediated excitotoxicity and reversed the NMDAR‐mediated regulation of ERK1/2 in rat hippocampal slices. The same protection was also observed in an animal model of ischemic stroke, alleviating brain infarction and yielding better motor recovery. These findings confirmed the existence of a receptor‐receptor interaction between NMDAR and mGluR1, implicating this interconnection as a potential treatment target site for ischemic stroke.—Lai, T. K. Y., Zhai, D. Su, P., Jiang, A., Boychuk, J., Liu, F. The receptor‐receptor interaction between mGluR1 receptor and NMDA receptor: a potential therapeutic target for protection against ischemic stroke. FASEB J. 33, 14423‐14439 (2019). www.fasebj.org
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".